Overview

Dataset statistics

Number of variables38
Number of observations815
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory248.3 KiB
Average record size in memory312.0 B

Variable types

Numeric9
Categorical29

Alerts

AnnualSpending is highly overall correlated with IncomeEstimateHigh correlation
Female is highly overall correlated with MaleHigh correlation
IncomeEstimate is highly overall correlated with AnnualSpendingHigh correlation
Male is highly overall correlated with FemaleHigh correlation
HasComplaint is highly imbalanced (70.2%)Imbalance
Indianapolis is highly imbalanced (68.7%)Imbalance
San Antonio is highly imbalanced (75.5%)Imbalance
Jacksonville is highly imbalanced (70.2%)Imbalance
Fort Worth is highly imbalanced (70.2%)Imbalance
Washington is highly imbalanced (69.2%)Imbalance
Houston is highly imbalanced (73.3%)Imbalance
San Jose is highly imbalanced (73.9%)Imbalance
Chicago is highly imbalanced (72.8%)Imbalance
Dallas is highly imbalanced (72.3%)Imbalance
Charlotte is highly imbalanced (66.7%)Imbalance
Los Angeles is highly imbalanced (68.2%)Imbalance
San Diego is highly imbalanced (70.2%)Imbalance
Phoenix is highly imbalanced (65.3%)Imbalance
Philadelphia is highly imbalanced (71.8%)Imbalance
New York is highly imbalanced (70.2%)Imbalance
Austin is highly imbalanced (70.2%)Imbalance
Denver is highly imbalanced (73.3%)Imbalance
Columbus is highly imbalanced (73.9%)Imbalance
Seattle is highly imbalanced (70.2%)Imbalance
San Francisco is highly imbalanced (83.4%)Imbalance
IncomeEstimate has unique valuesUnique
AnnualSpending has unique valuesUnique
RiskScore has 13 (1.6%) zerosZeros
LoyaltyYears has 36 (4.4%) zerosZeros
ComplaintSatisfaction has 96 (11.8%) zerosZeros

Reproduction

Analysis started2024-06-30 10:49:12.982740
Analysis finished2024-06-30 10:49:17.968806
Duration4.99 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

RiskScore
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.426994
Minimum0
Maximum100
Zeros13
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.002510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q125
median51
Q376
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.975162
Coefficient of variation (CV)0.5944269
Kurtosis-1.1986858
Mean50.426994
Median Absolute Deviation (MAD)26
Skewness-0.0046910319
Sum41098
Variance898.51033
MonotonicityNot monotonic
2024-06-30T13:49:18.060671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 16
 
2.0%
61 16
 
2.0%
98 15
 
1.8%
88 14
 
1.7%
91 14
 
1.7%
95 13
 
1.6%
32 13
 
1.6%
0 13
 
1.6%
1 13
 
1.6%
100 13
 
1.6%
Other values (91) 675
82.8%
ValueCountFrequency (%)
0 13
1.6%
1 13
1.6%
2 9
1.1%
3 10
1.2%
4 9
1.1%
5 5
 
0.6%
6 6
0.7%
7 10
1.2%
8 9
1.1%
9 3
 
0.4%
ValueCountFrequency (%)
100 13
1.6%
99 5
 
0.6%
98 15
1.8%
97 5
 
0.6%
96 9
1.1%
95 13
1.6%
94 8
1.0%
93 6
 
0.7%
92 11
1.3%
91 14
1.7%

Age
Real number (ℝ)

Distinct53
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.596319
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.115535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median45
Q358
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.407973
Coefficient of variation (CV)0.34549877
Kurtosis-1.2101318
Mean44.596319
Median Absolute Deviation (MAD)13
Skewness-0.055947881
Sum36346
Variance237.40564
MonotonicityNot monotonic
2024-06-30T13:49:18.170477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 23
 
2.8%
66 22
 
2.7%
38 21
 
2.6%
64 21
 
2.6%
62 21
 
2.6%
21 20
 
2.5%
52 20
 
2.5%
36 20
 
2.5%
63 20
 
2.5%
68 19
 
2.3%
Other values (43) 608
74.6%
ValueCountFrequency (%)
18 16
2.0%
19 12
1.5%
20 16
2.0%
21 20
2.5%
22 15
1.8%
23 16
2.0%
24 11
1.3%
25 17
2.1%
26 14
1.7%
27 11
1.3%
ValueCountFrequency (%)
70 17
2.1%
69 14
1.7%
68 19
2.3%
67 9
1.1%
66 22
2.7%
65 13
1.6%
64 21
2.6%
63 20
2.5%
62 21
2.6%
61 15
1.8%

LoyaltyYears
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.096933
Minimum0
Maximum20
Zeros36
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.217789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0376931
Coefficient of variation (CV)0.597973
Kurtosis-1.2105115
Mean10.096933
Median Absolute Deviation (MAD)5
Skewness-0.042539867
Sum8229
Variance36.453738
MonotonicityNot monotonic
2024-06-30T13:49:18.265631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
16 53
 
6.5%
12 49
 
6.0%
3 47
 
5.8%
14 47
 
5.8%
8 46
 
5.6%
1 41
 
5.0%
17 40
 
4.9%
9 39
 
4.8%
20 39
 
4.8%
10 38
 
4.7%
Other values (11) 376
46.1%
ValueCountFrequency (%)
0 36
4.4%
1 41
5.0%
2 35
4.3%
3 47
5.8%
4 37
4.5%
5 34
4.2%
6 29
3.6%
7 38
4.7%
8 46
5.6%
9 39
4.8%
ValueCountFrequency (%)
20 39
4.8%
19 34
4.2%
18 37
4.5%
17 40
4.9%
16 53
6.5%
15 35
4.3%
14 47
5.8%
13 29
3.6%
12 49
6.0%
11 32
3.9%

AccountBalance
Real number (ℝ)

Distinct811
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50618.514
Minimum1307
Maximum99618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.321228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1307
5-th percentile6351.5
Q127123
median49225
Q374984.5
95-th percentile94421.2
Maximum99618
Range98311
Interquartile range (IQR)47861.5

Descriptive statistics

Standard deviation27834.596
Coefficient of variation (CV)0.54988964
Kurtosis-1.1443434
Mean50618.514
Median Absolute Deviation (MAD)23830
Skewness0.031163703
Sum41254089
Variance7.7476476 × 108
MonotonicityNot monotonic
2024-06-30T13:49:18.377360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81072 2
 
0.2%
45386 2
 
0.2%
44856 2
 
0.2%
28526 2
 
0.2%
66785 1
 
0.1%
24929 1
 
0.1%
67163 1
 
0.1%
14780 1
 
0.1%
7589 1
 
0.1%
35812 1
 
0.1%
Other values (801) 801
98.3%
ValueCountFrequency (%)
1307 1
0.1%
1570 1
0.1%
1917 1
0.1%
2051 1
0.1%
2587 1
0.1%
2609 1
0.1%
2657 1
0.1%
2814 1
0.1%
2881 1
0.1%
2961 1
0.1%
ValueCountFrequency (%)
99618 1
0.1%
99614 1
0.1%
99530 1
0.1%
99371 1
0.1%
99300 1
0.1%
99132 1
0.1%
99046 1
0.1%
98905 1
0.1%
98859 1
0.1%
98790 1
0.1%

ProductCount
Real number (ℝ)

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4920245
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.421872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8021793
Coefficient of variation (CV)0.51022702
Kurtosis-1.1716063
Mean5.4920245
Median Absolute Deviation (MAD)2
Skewness-0.0049642198
Sum4476
Variance7.852209
MonotonicityNot monotonic
2024-06-30T13:49:18.461120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 102
12.5%
4 92
11.3%
7 91
11.2%
9 87
10.7%
2 83
10.2%
3 78
9.6%
1 74
9.1%
10 71
8.7%
8 69
8.5%
5 68
8.3%
ValueCountFrequency (%)
1 74
9.1%
2 83
10.2%
3 78
9.6%
4 92
11.3%
5 68
8.3%
6 102
12.5%
7 91
11.2%
8 69
8.5%
9 87
10.7%
10 71
8.7%
ValueCountFrequency (%)
10 71
8.7%
9 87
10.7%
8 69
8.5%
7 91
11.2%
6 102
12.5%
5 68
8.3%
4 92
11.3%
3 78
9.6%
2 83
10.2%
1 74
9.1%

HasCreditCard
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1.0
427 
0.0
388 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2445
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 427
52.4%
0.0 388
47.6%

Length

2024-06-30T13:49:18.502091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:18.540527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 427
52.4%
0.0 388
47.6%

Most occurring characters

ValueCountFrequency (%)
0 1203
49.2%
. 815
33.3%
1 427
 
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1203
49.2%
. 815
33.3%
1 427
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1203
49.2%
. 815
33.3%
1 427
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1203
49.2%
. 815
33.3%
1 427
 
17.5%

ActiveStatus
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0.0
413 
1.0
402 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2445
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 413
50.7%
1.0 402
49.3%

Length

2024-06-30T13:49:18.580491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:18.618196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 413
50.7%
1.0 402
49.3%

Most occurring characters

ValueCountFrequency (%)
0 1228
50.2%
. 815
33.3%
1 402
 
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1228
50.2%
. 815
33.3%
1 402
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1228
50.2%
. 815
33.3%
1 402
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1228
50.2%
. 815
33.3%
1 402
 
16.4%

IncomeEstimate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111721.21
Minimum20028
Maximum199965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:18.664522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20028
5-th percentile28208.1
Q169019.5
median113286
Q3155865
95-th percentile190614.9
Maximum199965
Range179937
Interquartile range (IQR)86845.5

Descriptive statistics

Standard deviation51123.79
Coefficient of variation (CV)0.45760147
Kurtosis-1.1237471
Mean111721.21
Median Absolute Deviation (MAD)43205
Skewness-0.073860255
Sum91052786
Variance2.6136419 × 109
MonotonicityNot monotonic
2024-06-30T13:49:18.721315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122511 1
 
0.1%
154365 1
 
0.1%
74703 1
 
0.1%
67819 1
 
0.1%
179541 1
 
0.1%
154917 1
 
0.1%
130186 1
 
0.1%
43290 1
 
0.1%
132551 1
 
0.1%
181771 1
 
0.1%
Other values (805) 805
98.8%
ValueCountFrequency (%)
20028 1
0.1%
20368 1
0.1%
20453 1
0.1%
20595 1
0.1%
20727 1
0.1%
20767 1
0.1%
20834 1
0.1%
21214 1
0.1%
21468 1
0.1%
21541 1
0.1%
ValueCountFrequency (%)
199965 1
0.1%
199593 1
0.1%
199412 1
0.1%
199006 1
0.1%
198780 1
0.1%
198428 1
0.1%
198394 1
0.1%
198379 1
0.1%
198330 1
0.1%
198157 1
0.1%

AnnualSpending
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50390.319
Minimum-19585.153
Maximum113025.42
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.7%
Memory size12.7 KiB
2024-06-30T13:49:18.777756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-19585.153
5-th percentile14167.635
Q134797.474
median50293.985
Q366819.99
95-th percentile86537.213
Maximum113025.42
Range132610.57
Interquartile range (IQR)32022.516

Descriptive statistics

Standard deviation22187.937
Coefficient of variation (CV)0.44032142
Kurtosis-0.33178637
Mean50390.319
Median Absolute Deviation (MAD)15927.807
Skewness0.046176687
Sum41068110
Variance4.9230454 × 108
MonotonicityNot monotonic
2024-06-30T13:49:18.835306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57486.34231 1
 
0.1%
53551.08243 1
 
0.1%
35389.03897 1
 
0.1%
44716.09718 1
 
0.1%
81183.16517 1
 
0.1%
40027.63228 1
 
0.1%
71143.32184 1
 
0.1%
18389.8171 1
 
0.1%
59232.85343 1
 
0.1%
81485.01966 1
 
0.1%
Other values (805) 805
98.8%
ValueCountFrequency (%)
-19585.15291 1
0.1%
-4941.208309 1
0.1%
-2994.060408 1
0.1%
-2608.622151 1
0.1%
-2087.86471 1
0.1%
-674.4777504 1
0.1%
592.1282066 1
0.1%
1217.742098 1
0.1%
2146.616814 1
0.1%
2695.101825 1
0.1%
ValueCountFrequency (%)
113025.4215 1
0.1%
109221.0164 1
0.1%
109155.4414 1
0.1%
107626.5131 1
0.1%
107545.7933 1
0.1%
106196.4936 1
0.1%
105206.131 1
0.1%
102633.4915 1
0.1%
100493.8729 1
0.1%
100310.3679 1
0.1%

HasComplaint
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1.0
772 
0.0
 
43

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2445
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 772
94.7%
0.0 43
 
5.3%

Length

2024-06-30T13:49:18.940177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:18.977419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 772
94.7%
0.0 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 858
35.1%
. 815
33.3%
1 772
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 858
35.1%
. 815
33.3%
1 772
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 858
35.1%
. 815
33.3%
1 772
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 858
35.1%
. 815
33.3%
1 772
31.6%

ComplaintSatisfaction
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9516614
Minimum0
Maximum10
Zeros96
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:19.014656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2415294
Coefficient of variation (CV)0.6546347
Kurtosis-1.2574724
Mean4.9516614
Median Absolute Deviation (MAD)3
Skewness-0.026849202
Sum4035.604
Variance10.507513
MonotonicityNot monotonic
2024-06-30T13:49:19.056876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 96
11.8%
8 86
10.6%
10 74
9.1%
6 73
9.0%
9 72
8.8%
4 69
8.5%
2 69
8.5%
3 69
8.5%
7 67
8.2%
1 67
8.2%
Other values (2) 73
9.0%
ValueCountFrequency (%)
0 96
11.8%
1 67
8.2%
2 69
8.5%
3 69
8.5%
4 69
8.5%
4.950505051 8
 
1.0%
5 65
8.0%
6 73
9.0%
7 67
8.2%
8 86
10.6%
ValueCountFrequency (%)
10 74
9.1%
9 72
8.8%
8 86
10.6%
7 67
8.2%
6 73
9.0%
5 65
8.0%
4.950505051 8
 
1.0%
4 69
8.5%
3 69
8.5%
2 69
8.5%

CreditCardPoints
Real number (ℝ)

Distinct748
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2441.492
Minimum2
Maximum4984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2024-06-30T13:49:19.105669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile226.7
Q11137.5
median2394
Q33670.5
95-th percentile4751.1
Maximum4984
Range4982
Interquartile range (IQR)2533

Descriptive statistics

Standard deviation1460.7422
Coefficient of variation (CV)0.59829899
Kurtosis-1.1888531
Mean2441.492
Median Absolute Deviation (MAD)1268
Skewness0.060839424
Sum1989816
Variance2133767.8
MonotonicityNot monotonic
2024-06-30T13:49:19.161399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784 3
 
0.4%
761 3
 
0.4%
1914 3
 
0.4%
3598 3
 
0.4%
2666 2
 
0.2%
1477 2
 
0.2%
3535 2
 
0.2%
552 2
 
0.2%
4916 2
 
0.2%
4798 2
 
0.2%
Other values (738) 791
97.1%
ValueCountFrequency (%)
2 1
0.1%
11 1
0.1%
23 1
0.1%
26 1
0.1%
36 1
0.1%
39 1
0.1%
43 1
0.1%
51 1
0.1%
56 1
0.1%
68 1
0.1%
ValueCountFrequency (%)
4984 1
0.1%
4971 1
0.1%
4970 1
0.1%
4969 1
0.1%
4960 1
0.1%
4952 1
0.1%
4944 1
0.1%
4923 1
0.1%
4916 2
0.2%
4913 1
0.1%

Indianapolis
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
769 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

Length

2024-06-30T13:49:19.209651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.248655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 769
94.4%
1 46
 
5.6%

San Antonio
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
782 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Length

2024-06-30T13:49:19.288703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.325714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 782
96.0%
1 33
 
4.0%

Jacksonville
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:19.364179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.402223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Fort Worth
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:19.441010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.479471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Washington
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
770 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Length

2024-06-30T13:49:19.518709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.557993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 770
94.5%
1 45
 
5.5%

Houston
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
778 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Length

2024-06-30T13:49:19.597861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.636064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

San Jose
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
779 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Length

2024-06-30T13:49:19.676856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.715175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Chicago
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
777 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Length

2024-06-30T13:49:19.756264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.794377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 777
95.3%
1 38
 
4.7%

Dallas
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
776 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Length

2024-06-30T13:49:19.834011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.873263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 776
95.2%
1 39
 
4.8%

Charlotte
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
765 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Length

2024-06-30T13:49:19.913273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:19.952519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 765
93.9%
1 50
 
6.1%

Los Angeles
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
768 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

Length

2024-06-30T13:49:19.992169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.029666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 768
94.2%
1 47
 
5.8%

San Diego
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:20.070172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.108049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Phoenix
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
762 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Length

2024-06-30T13:49:20.147925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.185551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 762
93.5%
1 53
 
6.5%

Philadelphia
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
775 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

Length

2024-06-30T13:49:20.224602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.263157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 775
95.1%
1 40
 
4.9%

New York
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:20.302225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.340610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Austin
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:20.379597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.417001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Denver
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
778 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Length

2024-06-30T13:49:20.457046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.494712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 778
95.5%
1 37
 
4.5%

Columbus
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
779 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Length

2024-06-30T13:49:20.535007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.625563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 779
95.6%
1 36
 
4.4%

Seattle
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
772 
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Length

2024-06-30T13:49:20.664594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.702627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 772
94.7%
1 43
 
5.3%

San Francisco
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
795 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Length

2024-06-30T13:49:20.741568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.779792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 795
97.5%
1 20
 
2.5%

Female
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
429 
0
386 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Length

2024-06-30T13:49:20.818522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.856078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Most occurring characters

ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 429
52.6%
0 386
47.4%

Male
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
429 
1
386 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

Length

2024-06-30T13:49:20.896859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:20.934425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

Most occurring characters

ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 429
52.6%
1 386
47.4%

American Express
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
599 
1
216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Length

2024-06-30T13:49:20.975365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:21.013117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 599
73.5%
1 216
 
26.5%

Discover
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
622 
1
193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

Length

2024-06-30T13:49:21.053841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:21.091636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

Most occurring characters

ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 622
76.3%
1 193
 
23.7%

MasterCard
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
616 
1
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Length

2024-06-30T13:49:21.131422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:21.170404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 616
75.6%
1 199
 
24.4%

Visa
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
608 
1
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Length

2024-06-30T13:49:21.210299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:21.248937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Most occurring characters

ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 608
74.6%
1 207
 
25.4%

Interactions

2024-06-30T13:49:17.246899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:13.988465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.445901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.864011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.259905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.671654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.037604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.486245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.870080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.286156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.049992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.540571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.907565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.300045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.710025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.079301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.528424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.910187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.325263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.124959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.578110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.949714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.341453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.750912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.119520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.570650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.951364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.370275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.177322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.622175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.997005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.415975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.796062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.219053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.617601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.997220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.410254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.237213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.661591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.039978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.460279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.836982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.262266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.658395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.038090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.449306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.281024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.700322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.082302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.501072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.876744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.304753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.699289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.078731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.492954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.324217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.742919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.128701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.547659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.919661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.351722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.743931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.123079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.536418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.367256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.785252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.174362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.592498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.963026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.398898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.787082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.167104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.576330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.406187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:14.824412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.216330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.631510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:15.999760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.442396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:16.827895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:17.205943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-30T13:49:21.296077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AccountBalanceActiveStatusAgeAmerican ExpressAnnualSpendingAustinCharlotteChicagoColumbusComplaintSatisfactionCreditCardPointsDallasDenverDiscoverFemaleFort WorthHasComplaintHasCreditCardHoustonIncomeEstimateIndianapolisJacksonvilleLos AngelesLoyaltyYearsMaleMasterCardNew YorkPhiladelphiaPhoenixProductCountRiskScoreSan AntonioSan DiegoSan FranciscoSan JoseSeattleVisaWashington
AccountBalance1.0000.082-0.0400.0000.2610.0000.0000.0370.0630.035-0.0220.0270.0360.0000.0000.0250.0650.0000.000-0.0290.0590.0840.0000.0320.0000.0480.0700.0360.0000.012-0.0040.0000.0000.0540.0000.0000.0000.069
ActiveStatus0.0821.0000.0540.0000.0600.0600.0000.0000.000-0.034-0.0050.0000.0000.0330.0000.0000.0000.0280.0270.0200.0370.0000.0000.0770.0000.0000.0000.0000.0000.004-0.0240.0000.0090.0000.0000.0890.0000.000
Age-0.0400.0541.0000.0320.0580.0000.0140.0000.052-0.010-0.0650.0300.0000.0000.0000.0000.0380.0000.0830.0680.0000.0000.0970.0590.0000.0000.0000.0000.0680.0220.0360.0000.1060.0000.0000.0000.0000.000
American Express0.0000.0000.0321.000-0.0560.0000.0000.0000.000-0.0200.0030.0000.0000.3300.0140.0000.0000.0000.035-0.0540.0540.0000.062-0.0770.0140.3360.0000.0000.000-0.0130.0210.0170.0080.0000.0000.0000.3460.000
AnnualSpending0.2610.0600.058-0.0561.0000.0000.0600.0000.000-0.029-0.0020.0760.0320.0000.0690.0300.0880.0000.0990.8190.0000.1410.000-0.0580.0690.0000.0000.0000.000-0.005-0.0570.0000.0000.0000.0000.0000.0000.000
Austin0.0000.0600.0000.0000.0001.0000.0340.0180.0130.0080.0020.0190.0150.0000.0000.0260.0000.0000.015-0.0310.0300.0260.0310.0690.0000.0000.0260.0210.037-0.0540.0060.0000.0260.0000.0130.0260.0000.028
Charlotte0.0000.0000.0140.0000.0600.0341.0000.0270.024-0.0200.0090.0290.0260.0200.0000.0340.0000.0140.026-0.0060.0380.0340.039-0.0860.0000.0000.0340.0300.0450.0350.0010.0180.0340.0000.0240.0340.0580.037
Chicago0.0370.0000.0000.0000.0000.0180.0271.0000.0000.0070.0100.0080.0000.0000.0000.0180.0000.0000.0000.0090.0220.0180.024-0.0220.0000.0610.0180.0110.0310.071-0.0040.0000.0180.0000.0000.0180.0000.021
Columbus0.0630.0000.0520.0000.0000.0130.0240.0001.000-0.026-0.0520.0000.0000.0920.0240.0130.0000.0000.0000.0070.0190.0130.020-0.0210.0240.0000.0130.0000.028-0.013-0.0650.0000.0130.0000.0000.0130.0360.017
ComplaintSatisfaction0.035-0.034-0.010-0.020-0.0290.008-0.0200.007-0.0261.0000.0160.0460.0000.0000.1280.0360.4340.0000.087-0.0250.0000.1070.046-0.0040.1280.0490.0000.0000.003-0.026-0.0390.0000.0320.1510.0000.0960.0000.000
CreditCardPoints-0.022-0.005-0.0650.003-0.0020.0020.0090.010-0.0520.0161.0000.0660.0460.0000.0000.0000.0000.0000.056-0.0130.0380.0000.073-0.0350.0000.0000.0000.0000.080-0.010-0.0130.0180.0000.0000.1390.0000.0490.060
Dallas0.0270.0000.0300.0000.0760.0190.0290.0080.0000.0460.0661.0000.0020.0000.0000.0190.0000.0000.0020.0190.0240.0190.0250.0220.0000.0000.0190.0140.032-0.062-0.0470.0000.0190.0000.0000.0190.0000.022
Denver0.0360.0000.0000.0000.0320.0150.0260.0000.0000.0000.0460.0021.0000.0000.0000.0150.0210.0000.000-0.0040.0200.0150.0220.0200.0000.0000.0150.0080.029-0.0040.0190.0000.0150.0000.0000.0150.0000.019
Discover0.0000.0330.0000.3300.0000.0000.0200.0000.0920.0000.0000.0000.0001.0000.0000.0250.0000.0000.0000.0370.0000.0740.0570.0010.0000.3110.0000.0190.007-0.0230.0020.0860.0250.0000.0000.0640.3200.000
Female0.0000.0000.0000.0140.0690.0000.0000.0000.0240.1280.0000.0000.0000.0001.0000.0000.0000.0150.0000.0350.0000.0000.0190.0040.9980.0070.0000.0000.0000.003-0.0050.0000.0000.0000.0000.0000.0000.000
Fort Worth0.0250.0000.0000.0000.0300.0260.0340.0180.0130.0360.0000.0190.0150.0250.0001.0000.0000.0000.015-0.0750.0300.0260.0310.0680.0000.0150.0260.0210.037-0.044-0.0550.0000.0260.0000.0130.0260.0000.028
HasComplaint0.0650.0000.0380.0000.0880.0000.0000.0000.0000.4340.0000.0000.0210.0000.0000.0001.0000.0000.000-0.0100.0000.0000.0000.0030.0000.0160.0000.0040.000-0.0710.0380.0000.0000.0000.0000.0000.0000.000
HasCreditCard0.0000.0280.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0150.0000.0001.0000.0000.0150.0310.0690.000-0.0330.0150.0000.0000.0000.052-0.0310.0800.0000.0000.0330.0200.0560.0000.066
Houston0.0000.0270.0830.0350.0990.0150.0260.0000.0000.0870.0560.0020.0000.0000.0000.0150.0000.0001.0000.0520.0200.0150.022-0.0380.0000.0000.0150.0080.029-0.0200.0380.0000.0150.0000.0000.0150.0000.019
IncomeEstimate-0.0290.0200.068-0.0540.819-0.031-0.0060.0090.007-0.025-0.0130.019-0.0040.0370.035-0.075-0.0100.0150.0521.0000.0410.0000.000-0.0500.0070.0000.0850.0990.000-0.0110.0010.0000.0000.0560.0000.0000.0820.000
Indianapolis0.0590.0370.0000.0540.0000.0300.0380.0220.0190.0000.0380.0240.0200.0000.0000.0300.0000.0310.0200.0411.0000.0300.034-0.0330.0000.0000.0300.0250.0410.0720.0340.0110.0300.0000.0190.0300.0800.032
Jacksonville0.0840.0000.0000.0000.1410.0260.0340.0180.0130.1070.0000.0190.0150.0740.0000.0260.0000.0690.0150.0000.0301.0000.031-0.0450.0000.0370.0260.0210.037-0.0030.0220.0000.0260.0000.0130.0260.0000.028
Los Angeles0.0000.0000.0970.0620.0000.0310.0390.0240.0200.0460.0730.0250.0220.0570.0190.0310.0000.0000.0220.0000.0340.0311.000-0.0210.0190.0340.0310.0270.0420.0280.0430.0130.0310.0000.0200.0310.0430.033
LoyaltyYears0.0320.0770.059-0.077-0.0580.069-0.086-0.022-0.021-0.004-0.0350.0220.0200.0010.0040.0680.003-0.033-0.038-0.050-0.033-0.045-0.0211.0000.0000.1000.0000.0000.0000.0270.0270.0000.0000.0640.0470.0000.0380.041
Male0.0000.0000.0000.0140.0690.0000.0000.0000.0240.1280.0000.0000.0000.0000.9980.0000.0000.0150.0000.0070.0000.0000.0190.0001.0000.0070.0000.0000.000-0.0030.0050.0000.0000.0000.0000.0000.0000.000
MasterCard0.0480.0000.0000.3360.0000.0000.0000.0610.0000.0490.0000.0000.0000.3110.0070.0150.0160.0000.0000.0000.0000.0370.0340.1000.0071.0000.0000.0000.0540.059-0.0360.0000.0000.0000.0000.0000.3270.000
New York0.0700.0000.0000.0000.0000.0260.0340.0180.0130.0000.0000.0190.0150.0000.0000.0260.0000.0000.0150.0850.0300.0260.0310.0000.0000.0001.0000.0210.0370.036-0.0420.0000.0260.0000.0130.0260.0000.028
Philadelphia0.0360.0000.0000.0000.0000.0210.0300.0110.0000.0000.0000.0140.0080.0190.0000.0210.0040.0000.0080.0990.0250.0210.0270.0000.0000.0000.0211.0000.033-0.022-0.0100.0000.0210.0000.0000.0210.0260.024
Phoenix0.0000.0000.0680.0000.0000.0370.0450.0310.0280.0030.0800.0320.0290.0070.0000.0370.0000.0520.0290.0000.0410.0370.0420.0000.0000.0540.0370.0331.0000.0300.0450.0220.0370.0000.0280.0370.0000.040
ProductCount0.0120.0040.022-0.013-0.005-0.0540.0350.071-0.013-0.026-0.010-0.062-0.004-0.0230.003-0.044-0.071-0.031-0.020-0.0110.072-0.0030.0280.027-0.0030.0590.036-0.0220.0301.0000.0010.0500.0000.0420.0500.0380.0000.109
RiskScore-0.004-0.0240.0360.021-0.0570.0060.001-0.004-0.065-0.039-0.013-0.0470.0190.002-0.005-0.0550.0380.0800.0380.0010.0340.0220.0430.0270.005-0.036-0.042-0.0100.0450.0011.0000.0000.0000.0000.0000.0000.0000.000
San Antonio0.0000.0000.0000.0170.0000.0000.0180.0000.0000.0000.0180.0000.0000.0860.0000.0000.0000.0000.0000.0000.0110.0000.0130.0000.0000.0000.0000.0000.0220.0500.0001.0000.0000.0000.0000.0000.0000.008
San Diego0.0000.0090.1060.0080.0000.0260.0340.0180.0130.0320.0000.0190.0150.0250.0000.0260.0000.0000.0150.0000.0300.0260.0310.0000.0000.0000.0260.0210.0370.0000.0000.0001.0000.0000.0130.0260.0000.028
San Francisco0.0540.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.0000.0000.0000.0000.0330.0000.0560.0000.0000.0000.0640.0000.0000.0000.0000.0000.0420.0000.0000.0001.0000.0000.0000.0000.000
San Jose0.0000.0000.0000.0000.0000.0130.0240.0000.0000.0000.1390.0000.0000.0000.0000.0130.0000.0200.0000.0000.0190.0130.0200.0470.0000.0000.0130.0000.0280.0500.0000.0000.0130.0001.0000.0130.0300.017
Seattle0.0000.0890.0000.0000.0000.0260.0340.0180.0130.0960.0000.0190.0150.0640.0000.0260.0000.0560.0150.0000.0300.0260.0310.0000.0000.0000.0260.0210.0370.0380.0000.0000.0260.0000.0131.0000.0000.028
Visa0.0000.0000.0000.3460.0000.0000.0580.0000.0360.0000.0490.0000.0000.3200.0000.0000.0000.0000.0000.0820.0800.0000.0430.0380.0000.3270.0000.0260.0000.0000.0000.0000.0000.0000.0300.0001.0000.000
Washington0.0690.0000.0000.0000.0000.0280.0370.0210.0170.0000.0600.0220.0190.0000.0000.0280.0000.0660.0190.0000.0320.0280.0330.0410.0000.0000.0280.0240.0400.1090.0000.0080.0280.0000.0170.0280.0001.000

Missing values

2024-06-30T13:49:17.710079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-30T13:49:17.895327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RiskScoreAgeLoyaltyYearsAccountBalanceProductCountHasCreditCardActiveStatusIncomeEstimateAnnualSpendingHasComplaintComplaintSatisfactionCreditCardPointsIndianapolisSan AntonioJacksonvilleFort WorthWashingtonHoustonSan JoseChicagoDallasCharlotteLos AngelesSan DiegoPhoenixPhiladelphiaNew YorkAustinDenverColumbusSeattleSan FranciscoFemaleMaleAmerican ExpressDiscoverMasterCardVisa
051.036.010.033172.01.01.00.0122511.057486.3423141.010.0000004181.010000000000000000000101000
192.066.02.060197.08.00.01.0113144.029617.1693701.010.0000004858.001000000000000000000101000
214.063.07.059847.04.00.00.084824.053352.9441191.04.950505389.000100000000000000000100100
371.036.02.012577.05.00.01.043525.07072.4329531.04.000000718.010000000000000000000100100
460.039.013.078299.08.01.00.077804.054096.2274681.06.0000004244.000010000000000000000010100
520.041.012.021176.05.01.00.0166115.071312.7878071.02.0000004678.000001000000000000000100010
682.062.012.045386.010.00.01.061846.020237.6291441.08.000000120.000000100000000000000100100
786.029.01.064972.04.00.00.097261.055154.1495241.07.0000002652.000000010000000000000011000
874.047.012.090791.06.01.01.0188455.086032.3675911.02.0000002303.000000001000000000000010100
974.055.018.042003.05.01.00.049483.0592.1282071.04.0000004861.000000000100000000000010001
RiskScoreAgeLoyaltyYearsAccountBalanceProductCountHasCreditCardActiveStatusIncomeEstimateAnnualSpendingHasComplaintComplaintSatisfactionCreditCardPointsIndianapolisSan AntonioJacksonvilleFort WorthWashingtonHoustonSan JoseChicagoDallasCharlotteLos AngelesSan DiegoPhoenixPhiladelphiaNew YorkAustinDenverColumbusSeattleSan FranciscoFemaleMaleAmerican ExpressDiscoverMasterCardVisa
9882.052.010.033766.06.00.01.0102212.027176.8086411.02.02639.000000000000000010000011000
98922.019.01.080943.01.00.01.095379.055852.2401031.01.04582.000000000001000000000100001
99017.057.016.033006.01.00.01.065591.025514.2248851.02.03909.000000000000000000010101000
99137.040.08.032422.08.00.01.081750.041239.1745541.00.04801.001000000000000000000101000
99298.026.01.013205.07.01.01.0110648.026972.7311001.07.01164.000000000010000000000100001
99463.063.015.024527.06.01.01.0186526.068421.9656871.01.03066.000000000000000000010101000
99588.031.03.019443.09.01.01.0118537.040494.2813391.07.04872.000100000000000000000100001
996100.021.07.017524.07.00.01.082672.019895.8179651.05.0446.000000000000001000000100010
99873.048.04.066552.06.01.01.034429.040553.9553031.010.0208.000010000000000000000011000
99938.055.015.047142.04.00.01.0104294.051282.5131650.00.02460.000001000000000000000101000